https://github.com/jxz542189/dmn_plus
Dynamic Memory Networks for Visual and Textual Question Answering
Papers with Code 아카이브
Visual Question Answering (VQA)
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Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.
Image Source: visualqa.org
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jy0205/lavit
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
https://github.com/jzhang38/EasyContext
https://github.com/kahnchana/locvlm
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs
https://github.com/kaihe-better/llm-for-healthcare
https://github.com/kakao/DAFT
https://github.com/kakaobrain/honeybee
https://github.com/kalelpark/Mixture-of-Granularity-Specific-Experts-for-Fine-Grained-Categorization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/kangkang59812/GraphCaption
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/karandesaiii/CheXNet
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/karunraju/VQA
https://github.com/kazuto1011/grad-cam-pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/kdaip/stabletts
https://github.com/kdexd/lang-emerge-parlai
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/kdr/videorag-mrr2024
https://github.com/ken-power/SelfDrivingCarND-ExtendedKalmanFilter
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
https://github.com/ken-power/SensorFusionND-3D-Object-Tracking
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
https://github.com/keonlee9420/Daft-Exprt
https://github.com/kexinyi/ns-vqa
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
https://github.com/khanhnguyen21006/fldocvqa
https://github.com/kimhc6028/relational-networks
https://github.com/king-zark/self_critical
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/kjczarne/eager_gradcam_tf
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/klauscc/dam
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
https://github.com/knowlab/bi-weekly-paper-presentation
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
https://github.com/kyegomez/PALI
PaLI-X: On Scaling up a Multilingual Vision and Language Model
https://github.com/kyegomez/PALI3
https://github.com/kyegomez/PALM-E
https://github.com/kyegomez/RT-2
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
https://github.com/laihuihui/htc
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/lalithjets/global-reasoned-multi-task-model
Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
https://github.com/lamalab-org/chem-bench
Probing the limitations of multimodal language models for chemistry and materials research
https://github.com/lamalab-org/chembench
Probing the limitations of multimodal language models for chemistry and materials research
https://github.com/lastmile-ai/aiconfig
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models
https://github.com/lauradhatt/Interesting-Reads
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/lavender105/rsgpt
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.